Overview

Dataset statistics

Number of variables35
Number of observations195
Missing cells337
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory320.3 KiB
Average record size in memory1.6 KiB

Variable types

Categorical26
Numeric9

Alerts

Country has a high cardinality: 195 distinct valuesHigh cardinality
Density (P/Km2) has a high cardinality: 137 distinct valuesHigh cardinality
Abbreviation has a high cardinality: 188 distinct valuesHigh cardinality
Agricultural Land( %) has a high cardinality: 168 distinct valuesHigh cardinality
Land Area(Km2) has a high cardinality: 194 distinct valuesHigh cardinality
Armed Forces size has a high cardinality: 105 distinct valuesHigh cardinality
Capital/Major City has a high cardinality: 192 distinct valuesHigh cardinality
Co2-Emissions has a high cardinality: 184 distinct valuesHigh cardinality
CPI has a high cardinality: 175 distinct valuesHigh cardinality
CPI Change (%) has a high cardinality: 86 distinct valuesHigh cardinality
Currency-Code has a high cardinality: 133 distinct valuesHigh cardinality
Forested Area (%) has a high cardinality: 161 distinct valuesHigh cardinality
Gasoline Price has a high cardinality: 101 distinct valuesHigh cardinality
GDP has a high cardinality: 193 distinct valuesHigh cardinality
Gross primary education enrollment (%) has a high cardinality: 141 distinct valuesHigh cardinality
Gross tertiary education enrollment (%) has a high cardinality: 171 distinct valuesHigh cardinality
Largest city has a high cardinality: 188 distinct valuesHigh cardinality
Minimum wage has a high cardinality: 114 distinct valuesHigh cardinality
Official language has a high cardinality: 77 distinct valuesHigh cardinality
Out of pocket health expenditure has a high cardinality: 160 distinct valuesHigh cardinality
Population has a high cardinality: 194 distinct valuesHigh cardinality
Population: Labor force participation (%) has a high cardinality: 145 distinct valuesHigh cardinality
Tax revenue (%) has a high cardinality: 119 distinct valuesHigh cardinality
Total tax rate has a high cardinality: 156 distinct valuesHigh cardinality
Unemployment rate has a high cardinality: 164 distinct valuesHigh cardinality
Urban_population has a high cardinality: 190 distinct valuesHigh cardinality
Birth Rate is highly overall correlated with Fertility Rate and 5 other fieldsHigh correlation
Calling Code is highly overall correlated with Official languageHigh correlation
Fertility Rate is highly overall correlated with Birth Rate and 5 other fieldsHigh correlation
Infant mortality is highly overall correlated with Birth Rate and 5 other fieldsHigh correlation
Life expectancy is highly overall correlated with Birth Rate and 5 other fieldsHigh correlation
Maternal mortality ratio is highly overall correlated with Birth Rate and 5 other fieldsHigh correlation
Physicians per thousand is highly overall correlated with Birth Rate and 5 other fieldsHigh correlation
Latitude is highly overall correlated with Birth Rate and 5 other fieldsHigh correlation
Official language is highly overall correlated with Calling CodeHigh correlation
Abbreviation has 7 (3.6%) missing valuesMissing
Agricultural Land( %) has 7 (3.6%) missing valuesMissing
Armed Forces size has 24 (12.3%) missing valuesMissing
Birth Rate has 6 (3.1%) missing valuesMissing
Capital/Major City has 3 (1.5%) missing valuesMissing
Co2-Emissions has 7 (3.6%) missing valuesMissing
CPI has 17 (8.7%) missing valuesMissing
CPI Change (%) has 16 (8.2%) missing valuesMissing
Currency-Code has 15 (7.7%) missing valuesMissing
Fertility Rate has 7 (3.6%) missing valuesMissing
Forested Area (%) has 7 (3.6%) missing valuesMissing
Gasoline Price has 20 (10.3%) missing valuesMissing
GDP has 2 (1.0%) missing valuesMissing
Gross primary education enrollment (%) has 7 (3.6%) missing valuesMissing
Gross tertiary education enrollment (%) has 12 (6.2%) missing valuesMissing
Infant mortality has 6 (3.1%) missing valuesMissing
Largest city has 6 (3.1%) missing valuesMissing
Life expectancy has 8 (4.1%) missing valuesMissing
Maternal mortality ratio has 14 (7.2%) missing valuesMissing
Minimum wage has 45 (23.1%) missing valuesMissing
Out of pocket health expenditure has 7 (3.6%) missing valuesMissing
Physicians per thousand has 7 (3.6%) missing valuesMissing
Population: Labor force participation (%) has 19 (9.7%) missing valuesMissing
Tax revenue (%) has 26 (13.3%) missing valuesMissing
Total tax rate has 12 (6.2%) missing valuesMissing
Unemployment rate has 19 (9.7%) missing valuesMissing
Urban_population has 5 (2.6%) missing valuesMissing
Country is uniformly distributedUniform
Density (P/Km2) is uniformly distributedUniform
Abbreviation is uniformly distributedUniform
Agricultural Land( %) is uniformly distributedUniform
Land Area(Km2) is uniformly distributedUniform
Capital/Major City is uniformly distributedUniform
Co2-Emissions is uniformly distributedUniform
CPI is uniformly distributedUniform
Forested Area (%) is uniformly distributedUniform
Gasoline Price is uniformly distributedUniform
GDP is uniformly distributedUniform
Gross primary education enrollment (%) is uniformly distributedUniform
Gross tertiary education enrollment (%) is uniformly distributedUniform
Largest city is uniformly distributedUniform
Minimum wage is uniformly distributedUniform
Out of pocket health expenditure is uniformly distributedUniform
Population is uniformly distributedUniform
Population: Labor force participation (%) is uniformly distributedUniform
Tax revenue (%) is uniformly distributedUniform
Total tax rate is uniformly distributedUniform
Unemployment rate is uniformly distributedUniform
Urban_population is uniformly distributedUniform
Country has unique valuesUnique

Reproduction

Analysis started2023-07-30 00:09:01.342521
Analysis finished2023-07-30 00:09:26.409475
Duration25.07 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
Afghanistan
 
1
Saint Lucia
 
1
Nicaragua
 
1
Niger
 
1
Nigeria
 
1
Other values (190)
190 

Length

Max length32
Median length22
Mean length8.8615385
Min length4

Characters and Unicode

Total characters1728
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Afghanistan 1
 
0.5%
Saint Lucia 1
 
0.5%
Nicaragua 1
 
0.5%
Niger 1
 
0.5%
Nigeria 1
 
0.5%
North Korea 1
 
0.5%
North Macedonia 1
 
0.5%
Norway 1
 
0.5%
Oman 1
 
0.5%
Pakistan 1
 
0.5%
Other values (185) 185
94.9%

Length

2023-07-29T21:09:26.599965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 6
 
2.4%
the 5
 
2.0%
and 5
 
2.0%
of 4
 
1.6%
south 3
 
1.2%
guinea 3
 
1.2%
saint 3
 
1.2%
united 3
 
1.2%
states 2
 
0.8%
congo 2
 
0.8%
Other values (214) 219
85.9%

Most occurring characters

ValueCountFrequency (%)
a 260
15.0%
i 151
 
8.7%
n 133
 
7.7%
e 117
 
6.8%
r 93
 
5.4%
o 92
 
5.3%
t 75
 
4.3%
u 68
 
3.9%
60
 
3.5%
l 60
 
3.5%
Other values (43) 619
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1412
81.7%
Uppercase Letter 244
 
14.1%
Space Separator 60
 
3.5%
Other Symbol 11
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 260
18.4%
i 151
10.7%
n 133
9.4%
e 117
 
8.3%
r 93
 
6.6%
o 92
 
6.5%
t 75
 
5.3%
u 68
 
4.8%
l 60
 
4.2%
s 54
 
3.8%
Other values (16) 309
21.9%
Uppercase Letter
ValueCountFrequency (%)
S 30
 
12.3%
M 20
 
8.2%
C 19
 
7.8%
B 19
 
7.8%
A 16
 
6.6%
T 14
 
5.7%
N 14
 
5.7%
G 14
 
5.7%
L 12
 
4.9%
I 11
 
4.5%
Other values (14) 75
30.7%
Space Separator
ValueCountFrequency (%)
60
100.0%
Other Symbol
ValueCountFrequency (%)
� 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1656
95.8%
Common 72
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 260
15.7%
i 151
 
9.1%
n 133
 
8.0%
e 117
 
7.1%
r 93
 
5.6%
o 92
 
5.6%
t 75
 
4.5%
u 68
 
4.1%
l 60
 
3.6%
s 54
 
3.3%
Other values (40) 553
33.4%
Common
ValueCountFrequency (%)
60
83.3%
� 11
 
15.3%
- 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1717
99.4%
Specials 11
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 260
15.1%
i 151
 
8.8%
n 133
 
7.7%
e 117
 
6.8%
r 93
 
5.4%
o 92
 
5.4%
t 75
 
4.4%
u 68
 
4.0%
60
 
3.5%
l 60
 
3.5%
Other values (42) 608
35.4%
Specials
ValueCountFrequency (%)
� 11
100.0%

Density (P/Km2)
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct137
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
25
 
7
18
 
5
83
 
5
4
 
5
16
 
3
Other values (132)
170 

Length

Max length6
Median length5
Mean length2.4615385
Min length1

Characters and Unicode

Total characters480
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)50.8%

Sample

1st row60
2nd row105
3rd row18
4th row164
5th row26

Common Values

ValueCountFrequency (%)
25 7
 
3.6%
18 5
 
2.6%
83 5
 
2.6%
4 5
 
2.6%
16 3
 
1.5%
26 3
 
1.5%
17 3
 
1.5%
3 3
 
1.5%
20 3
 
1.5%
137 3
 
1.5%
Other values (127) 155
79.5%

Length

2023-07-29T21:09:26.838326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 7
 
3.6%
83 5
 
2.6%
4 5
 
2.6%
18 5
 
2.6%
16 3
 
1.5%
26 3
 
1.5%
17 3
 
1.5%
3 3
 
1.5%
20 3
 
1.5%
137 3
 
1.5%
Other values (127) 155
79.5%

Most occurring characters

ValueCountFrequency (%)
1 81
16.9%
3 65
13.5%
2 61
12.7%
4 47
9.8%
6 40
8.3%
5 39
8.1%
8 39
8.1%
7 37
7.7%
0 37
7.7%
9 27
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 473
98.5%
Other Punctuation 7
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 81
17.1%
3 65
13.7%
2 61
12.9%
4 47
9.9%
6 40
8.5%
5 39
8.2%
8 39
8.2%
7 37
7.8%
0 37
7.8%
9 27
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 81
16.9%
3 65
13.5%
2 61
12.7%
4 47
9.8%
6 40
8.3%
5 39
8.1%
8 39
8.1%
7 37
7.7%
0 37
7.7%
9 27
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 81
16.9%
3 65
13.5%
2 61
12.7%
4 47
9.8%
6 40
8.3%
5 39
8.1%
8 39
8.1%
7 37
7.7%
0 37
7.7%
9 27
 
5.6%

Abbreviation
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct188
Distinct (%)100.0%
Missing7
Missing (%)3.6%
Memory size11.2 KiB
AF
 
1
PY
 
1
NE
 
1
NG
 
1
KP
 
1
Other values (183)
183 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters376
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)100.0%

Sample

1st rowAF
2nd rowAL
3rd rowDZ
4th rowAD
5th rowAO

Common Values

ValueCountFrequency (%)
AF 1
 
0.5%
PY 1
 
0.5%
NE 1
 
0.5%
NG 1
 
0.5%
KP 1
 
0.5%
NO 1
 
0.5%
OM 1
 
0.5%
PK 1
 
0.5%
PW 1
 
0.5%
PA 1
 
0.5%
Other values (178) 178
91.3%
(Missing) 7
 
3.6%

Length

2023-07-29T21:09:27.014855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
af 1
 
0.5%
az 1
 
0.5%
ba 1
 
0.5%
dz 1
 
0.5%
ad 1
 
0.5%
ao 1
 
0.5%
ag 1
 
0.5%
ar 1
 
0.5%
am 1
 
0.5%
au 1
 
0.5%
Other values (178) 178
94.7%

Most occurring characters

ValueCountFrequency (%)
M 29
 
7.7%
S 25
 
6.6%
T 23
 
6.1%
G 22
 
5.9%
A 21
 
5.6%
B 21
 
5.6%
C 19
 
5.1%
L 19
 
5.1%
E 18
 
4.8%
N 18
 
4.8%
Other values (16) 161
42.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 376
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 29
 
7.7%
S 25
 
6.6%
T 23
 
6.1%
G 22
 
5.9%
A 21
 
5.6%
B 21
 
5.6%
C 19
 
5.1%
L 19
 
5.1%
E 18
 
4.8%
N 18
 
4.8%
Other values (16) 161
42.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 376
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 29
 
7.7%
S 25
 
6.6%
T 23
 
6.1%
G 22
 
5.9%
A 21
 
5.6%
B 21
 
5.6%
C 19
 
5.1%
L 19
 
5.1%
E 18
 
4.8%
N 18
 
4.8%
Other values (16) 161
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 29
 
7.7%
S 25
 
6.6%
T 23
 
6.1%
G 22
 
5.9%
A 21
 
5.6%
B 21
 
5.6%
C 19
 
5.1%
L 19
 
5.1%
E 18
 
4.8%
N 18
 
4.8%
Other values (16) 161
42.8%

Agricultural Land( %)
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct168
Distinct (%)89.4%
Missing7
Missing (%)3.6%
Memory size11.9 KiB
17.40%
 
3
73.40%
 
2
23.30%
 
2
39.30%
 
2
34.50%
 
2
Other values (163)
177 

Length

Max length6
Median length6
Mean length5.893617
Min length5

Characters and Unicode

Total characters1108
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)79.3%

Sample

1st row58.10%
2nd row43.10%
3rd row17.40%
4th row40.00%
5th row47.50%

Common Values

ValueCountFrequency (%)
17.40% 3
 
1.5%
73.40% 2
 
1.0%
23.30% 2
 
1.0%
39.30% 2
 
1.0%
34.50% 2
 
1.0%
31.10% 2
 
1.0%
71.50% 2
 
1.0%
64.80% 2
 
1.0%
23.10% 2
 
1.0%
2.70% 2
 
1.0%
Other values (158) 167
85.6%
(Missing) 7
 
3.6%

Length

2023-07-29T21:09:27.204350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17.40 3
 
1.6%
33.30 2
 
1.1%
73.40 2
 
1.1%
26.30 2
 
1.1%
71.70 2
 
1.1%
32.40 2
 
1.1%
25.60 2
 
1.1%
28.70 2
 
1.1%
42.00 2
 
1.1%
44.60 2
 
1.1%
Other values (158) 167
88.8%

Most occurring characters

ValueCountFrequency (%)
0 228
20.6%
. 188
17.0%
% 188
17.0%
4 80
 
7.2%
3 74
 
6.7%
2 64
 
5.8%
1 58
 
5.2%
7 56
 
5.1%
5 53
 
4.8%
6 48
 
4.3%
Other values (2) 71
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 732
66.1%
Other Punctuation 376
33.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228
31.1%
4 80
 
10.9%
3 74
 
10.1%
2 64
 
8.7%
1 58
 
7.9%
7 56
 
7.7%
5 53
 
7.2%
6 48
 
6.6%
8 42
 
5.7%
9 29
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 188
50.0%
% 188
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228
20.6%
. 188
17.0%
% 188
17.0%
4 80
 
7.2%
3 74
 
6.7%
2 64
 
5.8%
1 58
 
5.2%
7 56
 
5.1%
5 53
 
4.8%
6 48
 
4.3%
Other values (2) 71
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228
20.6%
. 188
17.0%
% 188
17.0%
4 80
 
7.2%
3 74
 
6.7%
2 64
 
5.8%
1 58
 
5.2%
7 56
 
5.1%
5 53
 
4.8%
6 48
 
4.3%
Other values (2) 71
 
6.4%

Land Area(Km2)
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct194
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Memory size12.2 KiB
652,230
 
1
616
 
1
130,370
 
1
1,267,000
 
1
923,768
 
1
Other values (189)
189 

Length

Max length10
Median length9
Mean length6.3865979
Min length1

Characters and Unicode

Total characters1239
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)100.0%

Sample

1st row652,230
2nd row28,748
3rd row2,381,741
4th row468
5th row1,246,700

Common Values

ValueCountFrequency (%)
652,230 1
 
0.5%
616 1
 
0.5%
130,370 1
 
0.5%
1,267,000 1
 
0.5%
923,768 1
 
0.5%
120,538 1
 
0.5%
25,713 1
 
0.5%
323,802 1
 
0.5%
309,500 1
 
0.5%
796,095 1
 
0.5%
Other values (184) 184
94.4%

Length

2023-07-29T21:09:27.401820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
652,230 1
 
0.5%
48,670 1
 
0.5%
13,880 1
 
0.5%
2,381,741 1
 
0.5%
468 1
 
0.5%
1,246,700 1
 
0.5%
443 1
 
0.5%
2,780,400 1
 
0.5%
29,743 1
 
0.5%
7,741,220 1
 
0.5%
Other values (184) 184
94.8%

Most occurring characters

ValueCountFrequency (%)
, 198
16.0%
0 155
12.5%
1 142
11.5%
2 113
9.1%
3 99
8.0%
8 99
8.0%
4 96
7.7%
7 92
7.4%
6 87
7.0%
5 85
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1041
84.0%
Other Punctuation 198
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155
14.9%
1 142
13.6%
2 113
10.9%
3 99
9.5%
8 99
9.5%
4 96
9.2%
7 92
8.8%
6 87
8.4%
5 85
8.2%
9 73
7.0%
Other Punctuation
ValueCountFrequency (%)
, 198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 198
16.0%
0 155
12.5%
1 142
11.5%
2 113
9.1%
3 99
8.0%
8 99
8.0%
4 96
7.7%
7 92
7.4%
6 87
7.0%
5 85
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 198
16.0%
0 155
12.5%
1 142
11.5%
2 113
9.1%
3 99
8.0%
8 99
8.0%
4 96
7.7%
7 92
7.4%
6 87
7.0%
5 85
6.9%

Armed Forces size
Categorical

HIGH CARDINALITY  MISSING 

Distinct105
Distinct (%)61.4%
Missing24
Missing (%)12.3%
Memory size11.4 KiB
2,000
 
7
1,000
 
6
16,000
 
5
0
 
5
4,000
 
5
Other values (100)
143 

Length

Max length9
Median length7
Mean length6.0292398
Min length1

Characters and Unicode

Total characters1031
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)42.1%

Sample

1st row323,000
2nd row9,000
3rd row317,000
4th row117,000
5th row0

Common Values

ValueCountFrequency (%)
2,000 7
 
3.6%
1,000 6
 
3.1%
16,000 5
 
2.6%
0 5
 
2.6%
4,000 5
 
2.6%
9,000 5
 
2.6%
21,000 4
 
2.1%
12,000 4
 
2.1%
6,000 3
 
1.5%
11,000 3
 
1.5%
Other values (95) 124
63.6%
(Missing) 24
 
12.3%

Length

2023-07-29T21:09:27.593307image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,000 7
 
4.1%
1,000 6
 
3.5%
16,000 5
 
2.9%
0 5
 
2.9%
4,000 5
 
2.9%
9,000 5
 
2.9%
21,000 4
 
2.3%
12,000 4
 
2.3%
22,000 3
 
1.8%
7,000 3
 
1.8%
Other values (95) 124
72.5%

Most occurring characters

ValueCountFrequency (%)
0 519
50.3%
, 171
 
16.6%
1 86
 
8.3%
2 64
 
6.2%
3 41
 
4.0%
4 31
 
3.0%
6 30
 
2.9%
5 27
 
2.6%
7 21
 
2.0%
8 21
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 860
83.4%
Other Punctuation 171
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 519
60.3%
1 86
 
10.0%
2 64
 
7.4%
3 41
 
4.8%
4 31
 
3.6%
6 30
 
3.5%
5 27
 
3.1%
7 21
 
2.4%
8 21
 
2.4%
9 20
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 519
50.3%
, 171
 
16.6%
1 86
 
8.3%
2 64
 
6.2%
3 41
 
4.0%
4 31
 
3.0%
6 30
 
2.9%
5 27
 
2.6%
7 21
 
2.0%
8 21
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 519
50.3%
, 171
 
16.6%
1 86
 
8.3%
2 64
 
6.2%
3 41
 
4.0%
4 31
 
3.0%
6 30
 
2.9%
5 27
 
2.6%
7 21
 
2.0%
8 21
 
2.0%

Birth Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct170
Distinct (%)89.9%
Missing6
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean20.214974
Minimum5.9
Maximum46.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:27.824691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile8.54
Q111.3
median17.95
Q328.75
95-th percentile37.922
Maximum46.08
Range40.18
Interquartile range (IQR)17.45

Descriptive statistics

Standard deviation9.9457738
Coefficient of variation (CV)0.49200033
Kurtosis-0.82102654
Mean20.214974
Median Absolute Deviation (MAD)7.49
Skewness0.57867151
Sum3820.63
Variance98.918416
MonotonicityNot monotonic
2023-07-29T21:09:28.198689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 3
 
1.5%
12 3
 
1.5%
10.9 2
 
1.0%
17.55 2
 
1.0%
16.75 2
 
1.0%
10.1 2
 
1.0%
13.99 2
 
1.0%
12.6 2
 
1.0%
9.7 2
 
1.0%
14 2
 
1.0%
Other values (160) 167
85.6%
(Missing) 6
 
3.1%
ValueCountFrequency (%)
5.9 1
0.5%
6.4 1
0.5%
6.8 1
0.5%
7.2 1
0.5%
7.3 1
0.5%
7.4 1
0.5%
7.9 1
0.5%
8.1 1
0.5%
8.11 1
0.5%
8.5 1
0.5%
ValueCountFrequency (%)
46.08 1
0.5%
42.17 1
0.5%
41.75 1
0.5%
41.54 1
0.5%
41.18 1
0.5%
40.73 1
0.5%
39.01 1
0.5%
38.54 1
0.5%
38.14 1
0.5%
37.93 1
0.5%

Calling Code
Real number (ℝ)

Distinct182
Distinct (%)93.8%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean360.54639
Minimum1
Maximum1876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:28.623555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q182.5
median255.5
Q3506.75
95-th percentile974.35
Maximum1876
Range1875
Interquartile range (IQR)424.25

Descriptive statistics

Standard deviation323.23642
Coefficient of variation (CV)0.89651825
Kurtosis1.5902054
Mean360.54639
Median Absolute Deviation (MAD)194
Skewness1.1916716
Sum69946
Variance104481.78
MonotonicityNot monotonic
2023-07-29T21:09:29.006531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
6.2%
7 2
 
1.0%
680 1
 
0.5%
64 1
 
0.5%
505 1
 
0.5%
227 1
 
0.5%
234 1
 
0.5%
850 1
 
0.5%
389 1
 
0.5%
47 1
 
0.5%
Other values (172) 172
88.2%
ValueCountFrequency (%)
1 12
6.2%
7 2
 
1.0%
20 1
 
0.5%
27 1
 
0.5%
30 1
 
0.5%
31 1
 
0.5%
32 1
 
0.5%
33 1
 
0.5%
34 1
 
0.5%
36 1
 
0.5%
ValueCountFrequency (%)
1876 1
0.5%
998 1
0.5%
996 1
0.5%
995 1
0.5%
994 1
0.5%
993 1
0.5%
992 1
0.5%
977 1
0.5%
976 1
0.5%
975 1
0.5%

Capital/Major City
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct192
Distinct (%)100.0%
Missing3
Missing (%)1.5%
Memory size12.8 KiB
Kabul
 
1
Wellington
 
1
Managua
 
1
Niamey
 
1
Abuja
 
1
Other values (187)
187 

Length

Max length22
Median length19
Mean length8.21875
Min length4

Characters and Unicode

Total characters1578
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)100.0%

Sample

1st rowKabul
2nd rowTirana
3rd rowAlgiers
4th rowAndorra la Vella
5th rowLuanda

Common Values

ValueCountFrequency (%)
Kabul 1
 
0.5%
Wellington 1
 
0.5%
Managua 1
 
0.5%
Niamey 1
 
0.5%
Abuja 1
 
0.5%
Pyongyang 1
 
0.5%
Skopje 1
 
0.5%
Oslo 1
 
0.5%
Muscat 1
 
0.5%
Islamabad 1
 
0.5%
Other values (182) 182
93.3%
(Missing) 3
 
1.5%

Length

2023-07-29T21:09:29.393496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 10
 
4.2%
port 4
 
1.7%
of 3
 
1.3%
san 3
 
1.3%
st 2
 
0.8%
baku 1
 
0.4%
luanda 1
 
0.4%
john's 1
 
0.4%
saint 1
 
0.4%
john 1
 
0.4%
Other values (209) 209
88.6%

Most occurring characters

ValueCountFrequency (%)
a 222
 
14.1%
o 110
 
7.0%
i 109
 
6.9%
n 97
 
6.1%
r 85
 
5.4%
e 84
 
5.3%
u 69
 
4.4%
t 68
 
4.3%
s 65
 
4.1%
l 50
 
3.2%
Other values (45) 619
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1251
79.3%
Uppercase Letter 236
 
15.0%
Space Separator 44
 
2.8%
Other Symbol 29
 
1.8%
Other Punctuation 15
 
1.0%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 222
17.7%
o 110
 
8.8%
i 109
 
8.7%
n 97
 
7.8%
r 85
 
6.8%
e 84
 
6.7%
u 69
 
5.5%
t 68
 
5.4%
s 65
 
5.2%
l 50
 
4.0%
Other values (15) 292
23.3%
Uppercase Letter
ValueCountFrequency (%)
B 28
11.9%
S 22
 
9.3%
M 21
 
8.9%
C 20
 
8.5%
A 18
 
7.6%
P 18
 
7.6%
D 15
 
6.4%
L 12
 
5.1%
K 11
 
4.7%
N 11
 
4.7%
Other values (14) 60
25.4%
Other Punctuation
ValueCountFrequency (%)
, 8
53.3%
. 4
26.7%
' 3
 
20.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Symbol
ValueCountFrequency (%)
� 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1487
94.2%
Common 91
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 222
14.9%
o 110
 
7.4%
i 109
 
7.3%
n 97
 
6.5%
r 85
 
5.7%
e 84
 
5.6%
u 69
 
4.6%
t 68
 
4.6%
s 65
 
4.4%
l 50
 
3.4%
Other values (39) 528
35.5%
Common
ValueCountFrequency (%)
44
48.4%
� 29
31.9%
, 8
 
8.8%
. 4
 
4.4%
' 3
 
3.3%
- 3
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549
98.2%
Specials 29
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 222
 
14.3%
o 110
 
7.1%
i 109
 
7.0%
n 97
 
6.3%
r 85
 
5.5%
e 84
 
5.4%
u 69
 
4.5%
t 68
 
4.4%
s 65
 
4.2%
l 50
 
3.2%
Other values (44) 590
38.1%
Specials
ValueCountFrequency (%)
� 29
100.0%

Co2-Emissions
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct184
Distinct (%)97.9%
Missing7
Missing (%)3.6%
Memory size11.8 KiB
143
 
2
495
 
2
28,284
 
2
2,017
 
2
7,407
 
1
Other values (179)
179 

Length

Max length9
Median length7
Mean length5.4521277
Min length2

Characters and Unicode

Total characters1025
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)95.7%

Sample

1st row8,672
2nd row4,536
3rd row150,006
4th row469
5th row34,693

Common Values

ValueCountFrequency (%)
143 2
 
1.0%
495 2
 
1.0%
28,284 2
 
1.0%
2,017 2
 
1.0%
7,407 1
 
0.5%
120,369 1
 
0.5%
41,023 1
 
0.5%
63,457 1
 
0.5%
201,150 1
 
0.5%
224 1
 
0.5%
Other values (174) 174
89.2%
(Missing) 7
 
3.6%

Length

2023-07-29T21:09:29.786446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
143 2
 
1.1%
2,017 2
 
1.1%
495 2
 
1.1%
28,284 2
 
1.1%
6,340 1
 
0.5%
21,848 1
 
0.5%
150,006 1
 
0.5%
469 1
 
0.5%
34,693 1
 
0.5%
557 1
 
0.5%
Other values (174) 174
92.6%

Most occurring characters

ValueCountFrequency (%)
, 163
15.9%
2 123
12.0%
1 110
10.7%
6 91
8.9%
4 85
8.3%
0 85
8.3%
3 81
7.9%
7 79
7.7%
8 75
7.3%
5 71
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 862
84.1%
Other Punctuation 163
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 123
14.3%
1 110
12.8%
6 91
10.6%
4 85
9.9%
0 85
9.9%
3 81
9.4%
7 79
9.2%
8 75
8.7%
5 71
8.2%
9 62
7.2%
Other Punctuation
ValueCountFrequency (%)
, 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1025
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 163
15.9%
2 123
12.0%
1 110
10.7%
6 91
8.9%
4 85
8.3%
0 85
8.3%
3 81
7.9%
7 79
7.7%
8 75
7.3%
5 71
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 163
15.9%
2 123
12.0%
1 110
10.7%
6 91
8.9%
4 85
8.3%
0 85
8.3%
3 81
7.9%
7 79
7.7%
8 75
7.3%
5 71
6.9%

CPI
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct175
Distinct (%)98.3%
Missing17
Missing (%)8.7%
Memory size11.6 KiB
110.62
 
2
99.55
 
2
106.58
 
2
149.9
 
1
114.24
 
1
Other values (170)
170 

Length

Max length8
Median length6
Mean length5.8876404
Min length3

Characters and Unicode

Total characters1048
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)96.6%

Sample

1st row149.9
2nd row119.05
3rd row151.36
4th row261.73
5th row113.81

Common Values

ValueCountFrequency (%)
110.62 2
 
1.0%
99.55 2
 
1.0%
106.58 2
 
1.0%
149.9 1
 
0.5%
114.24 1
 
0.5%
162.74 1
 
0.5%
109.32 1
 
0.5%
267.51 1
 
0.5%
120.27 1
 
0.5%
113.53 1
 
0.5%
Other values (165) 165
84.6%
(Missing) 17
 
8.7%

Length

2023-07-29T21:09:30.123542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
110.62 2
 
1.1%
106.58 2
 
1.1%
99.55 2
 
1.1%
107.43 1
 
0.6%
119.8 1
 
0.6%
167.4 1
 
0.6%
149.75 1
 
0.6%
104.9 1
 
0.6%
151.36 1
 
0.6%
261.73 1
 
0.6%
Other values (165) 165
92.7%

Most occurring characters

ValueCountFrequency (%)
1 271
25.9%
. 176
16.8%
2 90
 
8.6%
5 83
 
7.9%
3 74
 
7.1%
8 65
 
6.2%
6 62
 
5.9%
4 60
 
5.7%
0 55
 
5.2%
7 55
 
5.2%
Other values (2) 57
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 869
82.9%
Other Punctuation 179
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 271
31.2%
2 90
 
10.4%
5 83
 
9.6%
3 74
 
8.5%
8 65
 
7.5%
6 62
 
7.1%
4 60
 
6.9%
0 55
 
6.3%
7 55
 
6.3%
9 54
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 176
98.3%
, 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 271
25.9%
. 176
16.8%
2 90
 
8.6%
5 83
 
7.9%
3 74
 
7.1%
8 65
 
6.2%
6 62
 
5.9%
4 60
 
5.7%
0 55
 
5.2%
7 55
 
5.2%
Other values (2) 57
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 271
25.9%
. 176
16.8%
2 90
 
8.6%
5 83
 
7.9%
3 74
 
7.1%
8 65
 
6.2%
6 62
 
5.9%
4 60
 
5.7%
0 55
 
5.2%
7 55
 
5.2%
Other values (2) 57
 
5.4%

CPI Change (%)
Categorical

HIGH CARDINALITY  MISSING 

Distinct86
Distinct (%)48.0%
Missing16
Missing (%)8.2%
Memory size11.5 KiB
2.80%
 
7
1.80%
 
7
2.60%
 
6
2.30%
 
5
1.40%
 
5
Other values (81)
149 

Length

Max length7
Median length5
Mean length5.1843575
Min length5

Characters and Unicode

Total characters928
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)26.3%

Sample

1st row2.30%
2nd row1.40%
3rd row2.00%
4th row17.10%
5th row1.20%

Common Values

ValueCountFrequency (%)
2.80% 7
 
3.6%
1.80% 7
 
3.6%
2.60% 6
 
3.1%
2.30% 5
 
2.6%
1.40% 5
 
2.6%
0.80% 5
 
2.6%
1.00% 5
 
2.6%
2.10% 5
 
2.6%
1.60% 5
 
2.6%
2.50% 4
 
2.1%
Other values (76) 125
64.1%
(Missing) 16
 
8.2%

Length

2023-07-29T21:09:30.460639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.80 7
 
3.9%
1.80 7
 
3.9%
1.00 7
 
3.9%
2.60 6
 
3.4%
0.70 6
 
3.4%
0.40 6
 
3.4%
1.60 5
 
2.8%
2.50 5
 
2.8%
0.90 5
 
2.8%
2.10 5
 
2.8%
Other values (68) 120
67.0%

Most occurring characters

ValueCountFrequency (%)
0 230
24.8%
. 179
19.3%
% 179
19.3%
1 67
 
7.2%
2 58
 
6.2%
3 38
 
4.1%
7 30
 
3.2%
5 28
 
3.0%
8 27
 
2.9%
4 27
 
2.9%
Other values (3) 65
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 555
59.8%
Other Punctuation 358
38.6%
Dash Punctuation 15
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 230
41.4%
1 67
 
12.1%
2 58
 
10.5%
3 38
 
6.8%
7 30
 
5.4%
5 28
 
5.0%
8 27
 
4.9%
4 27
 
4.9%
6 26
 
4.7%
9 24
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 179
50.0%
% 179
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 928
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 230
24.8%
. 179
19.3%
% 179
19.3%
1 67
 
7.2%
2 58
 
6.2%
3 38
 
4.1%
7 30
 
3.2%
5 28
 
3.0%
8 27
 
2.9%
4 27
 
2.9%
Other values (3) 65
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 230
24.8%
. 179
19.3%
% 179
19.3%
1 67
 
7.2%
2 58
 
6.2%
3 38
 
4.1%
7 30
 
3.2%
5 28
 
3.0%
8 27
 
2.9%
4 27
 
2.9%
Other values (3) 65
 
7.0%

Currency-Code
Categorical

HIGH CARDINALITY  MISSING 

Distinct133
Distinct (%)73.9%
Missing15
Missing (%)7.7%
Memory size11.1 KiB
EUR
23 
XOF
 
8
XCD
 
6
USD
 
6
XAF
 
5
Other values (128)
132 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters540
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)70.0%

Sample

1st rowAFN
2nd rowALL
3rd rowDZD
4th rowEUR
5th rowAOA

Common Values

ValueCountFrequency (%)
EUR 23
 
11.8%
XOF 8
 
4.1%
XCD 6
 
3.1%
USD 6
 
3.1%
XAF 5
 
2.6%
AUD 4
 
2.1%
CHF 2
 
1.0%
AFN 1
 
0.5%
PLN 1
 
0.5%
PYG 1
 
0.5%
Other values (123) 123
63.1%
(Missing) 15
 
7.7%

Length

2023-07-29T21:09:30.798738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eur 23
 
12.8%
xof 8
 
4.4%
xcd 6
 
3.3%
usd 6
 
3.3%
xaf 5
 
2.8%
aud 4
 
2.2%
chf 2
 
1.1%
bbd 1
 
0.6%
bwp 1
 
0.6%
bam 1
 
0.6%
Other values (123) 123
68.3%

Most occurring characters

ValueCountFrequency (%)
D 52
 
9.6%
R 51
 
9.4%
U 44
 
8.1%
E 34
 
6.3%
S 31
 
5.7%
A 26
 
4.8%
N 25
 
4.6%
F 24
 
4.4%
K 21
 
3.9%
M 21
 
3.9%
Other values (16) 211
39.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 540
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 52
 
9.6%
R 51
 
9.4%
U 44
 
8.1%
E 34
 
6.3%
S 31
 
5.7%
A 26
 
4.8%
N 25
 
4.6%
F 24
 
4.4%
K 21
 
3.9%
M 21
 
3.9%
Other values (16) 211
39.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 52
 
9.6%
R 51
 
9.4%
U 44
 
8.1%
E 34
 
6.3%
S 31
 
5.7%
A 26
 
4.8%
N 25
 
4.6%
F 24
 
4.4%
K 21
 
3.9%
M 21
 
3.9%
Other values (16) 211
39.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 52
 
9.6%
R 51
 
9.4%
U 44
 
8.1%
E 34
 
6.3%
S 31
 
5.7%
A 26
 
4.8%
N 25
 
4.6%
F 24
 
4.4%
K 21
 
3.9%
M 21
 
3.9%
Other values (16) 211
39.1%

Fertility Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct139
Distinct (%)73.9%
Missing7
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean2.6981383
Minimum0.98
Maximum6.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:31.082974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.3105
Q11.705
median2.245
Q33.5975
95-th percentile5.1095
Maximum6.91
Range5.93
Interquartile range (IQR)1.8925

Descriptive statistics

Standard deviation1.2822675
Coefficient of variation (CV)0.47524157
Kurtosis-0.044592056
Mean2.6981383
Median Absolute Deviation (MAD)0.68
Skewness0.94764282
Sum507.25
Variance1.6442099
MonotonicityNot monotonic
2023-07-29T21:09:31.426058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 5
 
2.6%
1.62 4
 
2.1%
1.75 4
 
2.1%
1.71 3
 
1.5%
2.46 3
 
1.5%
1.56 3
 
1.5%
4.32 3
 
1.5%
1.26 3
 
1.5%
1.41 3
 
1.5%
2.42 3
 
1.5%
Other values (129) 154
79.0%
(Missing) 7
 
3.6%
ValueCountFrequency (%)
0.98 1
 
0.5%
1.14 1
 
0.5%
1.23 1
 
0.5%
1.26 3
1.5%
1.27 2
1.0%
1.29 1
 
0.5%
1.3 1
 
0.5%
1.33 1
 
0.5%
1.35 1
 
0.5%
1.37 1
 
0.5%
ValueCountFrequency (%)
6.91 1
0.5%
6.07 1
0.5%
5.92 1
0.5%
5.88 1
0.5%
5.75 1
0.5%
5.52 1
0.5%
5.41 1
0.5%
5.39 1
0.5%
5.22 1
0.5%
5.19 1
0.5%

Forested Area (%)
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct161
Distinct (%)85.6%
Missing7
Missing (%)3.6%
Memory size11.9 KiB
43.10%
 
3
33.20%
 
3
12.60%
 
3
32.70%
 
3
0.00%
 
3
Other values (156)
173 

Length

Max length6
Median length6
Mean length5.7712766
Min length5

Characters and Unicode

Total characters1085
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)73.9%

Sample

1st row2.10%
2nd row28.10%
3rd row0.80%
4th row34.00%
5th row46.30%

Common Values

ValueCountFrequency (%)
43.10% 3
 
1.5%
33.20% 3
 
1.5%
12.60% 3
 
1.5%
32.70% 3
 
1.5%
0.00% 3
 
1.5%
12.50% 2
 
1.0%
52.70% 2
 
1.0%
19.70% 2
 
1.0%
34.60% 2
 
1.0%
0.10% 2
 
1.0%
Other values (151) 163
83.6%
(Missing) 7
 
3.6%

Length

2023-07-29T21:09:31.727301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
43.10 3
 
1.6%
12.60 3
 
1.6%
32.70 3
 
1.6%
0.00 3
 
1.6%
33.20 3
 
1.6%
30.90 2
 
1.1%
14.70 2
 
1.1%
0.80 2
 
1.1%
0.20 2
 
1.1%
3.80 2
 
1.1%
Other values (151) 163
86.7%

Most occurring characters

ValueCountFrequency (%)
0 236
21.8%
. 188
17.3%
% 188
17.3%
1 76
 
7.0%
3 75
 
6.9%
2 59
 
5.4%
4 54
 
5.0%
5 45
 
4.1%
7 44
 
4.1%
6 41
 
3.8%
Other values (2) 79
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 709
65.3%
Other Punctuation 376
34.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 236
33.3%
1 76
 
10.7%
3 75
 
10.6%
2 59
 
8.3%
4 54
 
7.6%
5 45
 
6.3%
7 44
 
6.2%
6 41
 
5.8%
8 40
 
5.6%
9 39
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 188
50.0%
% 188
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1085
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 236
21.8%
. 188
17.3%
% 188
17.3%
1 76
 
7.0%
3 75
 
6.9%
2 59
 
5.4%
4 54
 
5.0%
5 45
 
4.1%
7 44
 
4.1%
6 41
 
3.8%
Other values (2) 79
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 236
21.8%
. 188
17.3%
% 188
17.3%
1 76
 
7.0%
3 75
 
6.9%
2 59
 
5.4%
4 54
 
5.0%
5 45
 
4.1%
7 44
 
4.1%
6 41
 
3.8%
Other values (2) 79
 
7.3%

Gasoline Price
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct101
Distinct (%)57.7%
Missing20
Missing (%)10.3%
Memory size11.5 KiB
$0.71
 
6
$1.16
 
5
$0.92
 
5
$0.98
 
4
$1.12
 
4
Other values (96)
151 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1050
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)31.4%

Sample

1st row$0.70
2nd row$1.36
3rd row$0.28
4th row$1.51
5th row$0.97

Common Values

ValueCountFrequency (%)
$0.71 6
 
3.1%
$1.16 5
 
2.6%
$0.92 5
 
2.6%
$0.98 4
 
2.1%
$1.12 4
 
2.1%
$0.83 3
 
1.5%
$0.90 3
 
1.5%
$1.36 3
 
1.5%
$1.03 3
 
1.5%
$1.32 3
 
1.5%
Other values (91) 136
69.7%
(Missing) 20
 
10.3%

Length

2023-07-29T21:09:32.018525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.71 6
 
3.4%
0.92 5
 
2.9%
1.16 5
 
2.9%
0.98 4
 
2.3%
1.12 4
 
2.3%
0.40 3
 
1.7%
1.40 3
 
1.7%
0.97 3
 
1.7%
1.10 3
 
1.7%
0.91 3
 
1.7%
Other values (91) 136
77.7%

Most occurring characters

ValueCountFrequency (%)
$ 175
16.7%
. 175
16.7%
175
16.7%
1 132
12.6%
0 129
12.3%
9 42
 
4.0%
4 36
 
3.4%
7 35
 
3.3%
2 35
 
3.3%
3 32
 
3.0%
Other values (3) 84
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 525
50.0%
Currency Symbol 175
 
16.7%
Other Punctuation 175
 
16.7%
Space Separator 175
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 132
25.1%
0 129
24.6%
9 42
 
8.0%
4 36
 
6.9%
7 35
 
6.7%
2 35
 
6.7%
3 32
 
6.1%
8 30
 
5.7%
6 29
 
5.5%
5 25
 
4.8%
Currency Symbol
ValueCountFrequency (%)
$ 175
100.0%
Other Punctuation
ValueCountFrequency (%)
. 175
100.0%
Space Separator
ValueCountFrequency (%)
175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
$ 175
16.7%
. 175
16.7%
175
16.7%
1 132
12.6%
0 129
12.3%
9 42
 
4.0%
4 36
 
3.4%
7 35
 
3.3%
2 35
 
3.3%
3 32
 
3.0%
Other values (3) 84
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
$ 175
16.7%
. 175
16.7%
175
16.7%
1 132
12.6%
0 129
12.3%
9 42
 
4.0%
4 36
 
3.4%
7 35
 
3.3%
2 35
 
3.3%
3 32
 
3.0%
Other values (3) 84
8.0%

GDP
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct193
Distinct (%)100.0%
Missing2
Missing (%)1.0%
Memory size14.0 KiB
$2,122,450,630
 
1
$206,928,765,544
 
1
$12,520,915,291
 
1
$12,928,145,120
 
1
$448,120,428,859
 
1
Other values (188)
188 

Length

Max length20
Median length19
Mean length16.108808
Min length12

Characters and Unicode

Total characters3109
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)100.0%

Sample

1st row$19,101,353,833
2nd row$15,278,077,447
3rd row$169,988,236,398
4th row$3,154,057,987
5th row$94,635,415,870

Common Values

ValueCountFrequency (%)
$2,122,450,630 1
 
0.5%
$206,928,765,544 1
 
0.5%
$12,520,915,291 1
 
0.5%
$12,928,145,120 1
 
0.5%
$448,120,428,859 1
 
0.5%
$32,100,000,000 1
 
0.5%
$10,220,781,069 1
 
0.5%
$403,336,363,636 1
 
0.5%
$76,983,094,928 1
 
0.5%
$304,400,000,000 1
 
0.5%
Other values (183) 183
93.8%
(Missing) 2
 
1.0%

Length

2023-07-29T21:09:32.343652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,122,450,630 1
 
0.5%
15,278,077,447 1
 
0.5%
12,827,000,000 1
 
0.5%
169,988,236,398 1
 
0.5%
3,154,057,987 1
 
0.5%
94,635,415,870 1
 
0.5%
1,727,759,259 1
 
0.5%
449,663,446,954 1
 
0.5%
13,672,802,158 1
 
0.5%
1,392,680,589,329 1
 
0.5%
Other values (183) 183
94.8%

Most occurring characters

ValueCountFrequency (%)
, 583
18.8%
0 322
10.4%
1 250
8.0%
4 227
 
7.3%
2 218
 
7.0%
8 199
 
6.4%
$ 193
 
6.2%
193
 
6.2%
3 192
 
6.2%
7 192
 
6.2%
Other values (3) 540
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2140
68.8%
Other Punctuation 583
 
18.8%
Currency Symbol 193
 
6.2%
Space Separator 193
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 322
15.0%
1 250
11.7%
4 227
10.6%
2 218
10.2%
8 199
9.3%
3 192
9.0%
7 192
9.0%
5 188
8.8%
9 177
8.3%
6 175
8.2%
Other Punctuation
ValueCountFrequency (%)
, 583
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 193
100.0%
Space Separator
ValueCountFrequency (%)
193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3109
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 583
18.8%
0 322
10.4%
1 250
8.0%
4 227
 
7.3%
2 218
 
7.0%
8 199
 
6.4%
$ 193
 
6.2%
193
 
6.2%
3 192
 
6.2%
7 192
 
6.2%
Other values (3) 540
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 583
18.8%
0 322
10.4%
1 250
8.0%
4 227
 
7.3%
2 218
 
7.0%
8 199
 
6.4%
$ 193
 
6.2%
193
 
6.2%
3 192
 
6.2%
7 192
 
6.2%
Other values (3) 540
17.4%

Gross primary education enrollment (%)
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct141
Distinct (%)75.0%
Missing7
Missing (%)3.6%
Memory size12.0 KiB
104.00%
 
4
100.90%
 
4
99.40%
 
3
97.20%
 
3
101.90%
 
3
Other values (136)
171 

Length

Max length7
Median length7
Mean length6.6861702
Min length6

Characters and Unicode

Total characters1257
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)58.0%

Sample

1st row104.00%
2nd row107.00%
3rd row109.90%
4th row106.40%
5th row113.50%

Common Values

ValueCountFrequency (%)
104.00% 4
 
2.1%
100.90% 4
 
2.1%
99.40% 3
 
1.5%
97.20% 3
 
1.5%
101.90% 3
 
1.5%
100.20% 3
 
1.5%
99.80% 3
 
1.5%
106.20% 3
 
1.5%
103.20% 3
 
1.5%
100.00% 3
 
1.5%
Other values (131) 156
80.0%
(Missing) 7
 
3.6%

Length

2023-07-29T21:09:32.690727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
104.00 4
 
2.1%
100.90 4
 
2.1%
106.20 3
 
1.6%
100.30 3
 
1.6%
100.40 3
 
1.6%
100.00 3
 
1.6%
103.20 3
 
1.6%
106.40 3
 
1.6%
99.80 3
 
1.6%
100.20 3
 
1.6%
Other values (131) 156
83.0%

Most occurring characters

ValueCountFrequency (%)
0 332
26.4%
. 188
15.0%
% 188
15.0%
1 182
14.5%
9 76
 
6.0%
4 51
 
4.1%
8 49
 
3.9%
2 48
 
3.8%
3 42
 
3.3%
6 37
 
2.9%
Other values (2) 64
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 881
70.1%
Other Punctuation 376
29.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 332
37.7%
1 182
20.7%
9 76
 
8.6%
4 51
 
5.8%
8 49
 
5.6%
2 48
 
5.4%
3 42
 
4.8%
6 37
 
4.2%
7 34
 
3.9%
5 30
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 188
50.0%
% 188
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1257
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 332
26.4%
. 188
15.0%
% 188
15.0%
1 182
14.5%
9 76
 
6.0%
4 51
 
4.1%
8 49
 
3.9%
2 48
 
3.8%
3 42
 
3.3%
6 37
 
2.9%
Other values (2) 64
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 332
26.4%
. 188
15.0%
% 188
15.0%
1 182
14.5%
9 76
 
6.0%
4 51
 
4.1%
8 49
 
3.9%
2 48
 
3.8%
3 42
 
3.3%
6 37
 
2.9%
Other values (2) 64
 
5.1%

Gross tertiary education enrollment (%)
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct171
Distinct (%)93.4%
Missing12
Missing (%)6.2%
Memory size11.7 KiB
10.20%
 
3
88.20%
 
2
12.80%
 
2
63.90%
 
2
14.10%
 
2
Other values (166)
172 

Length

Max length7
Median length6
Mean length5.8196721
Min length5

Characters and Unicode

Total characters1065
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)87.4%

Sample

1st row9.70%
2nd row55.00%
3rd row51.40%
4th row9.30%
5th row24.80%

Common Values

ValueCountFrequency (%)
10.20% 3
 
1.5%
88.20% 2
 
1.0%
12.80% 2
 
1.0%
63.90% 2
 
1.0%
14.10% 2
 
1.0%
9.00% 2
 
1.0%
23.70% 2
 
1.0%
11.60% 2
 
1.0%
65.60% 2
 
1.0%
9.30% 2
 
1.0%
Other values (161) 162
83.1%
(Missing) 12
 
6.2%

Length

2023-07-29T21:09:33.013861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.20 3
 
1.6%
23.70 2
 
1.1%
82.00 2
 
1.1%
9.30 2
 
1.1%
65.60 2
 
1.1%
11.60 2
 
1.1%
88.20 2
 
1.1%
9.00 2
 
1.1%
63.90 2
 
1.1%
12.80 2
 
1.1%
Other values (161) 162
88.5%

Most occurring characters

ValueCountFrequency (%)
0 223
20.9%
. 183
17.2%
% 183
17.2%
1 81
 
7.6%
6 61
 
5.7%
4 55
 
5.2%
2 54
 
5.1%
3 49
 
4.6%
8 48
 
4.5%
5 47
 
4.4%
Other values (2) 81
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 699
65.6%
Other Punctuation 366
34.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223
31.9%
1 81
 
11.6%
6 61
 
8.7%
4 55
 
7.9%
2 54
 
7.7%
3 49
 
7.0%
8 48
 
6.9%
5 47
 
6.7%
7 45
 
6.4%
9 36
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 183
50.0%
% 183
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1065
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223
20.9%
. 183
17.2%
% 183
17.2%
1 81
 
7.6%
6 61
 
5.7%
4 55
 
5.2%
2 54
 
5.1%
3 49
 
4.6%
8 48
 
4.5%
5 47
 
4.4%
Other values (2) 81
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223
20.9%
. 183
17.2%
% 183
17.2%
1 81
 
7.6%
6 61
 
5.7%
4 55
 
5.2%
2 54
 
5.1%
3 49
 
4.6%
8 48
 
4.5%
5 47
 
4.4%
Other values (2) 81
 
7.6%

Infant mortality
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct144
Distinct (%)76.2%
Missing6
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean21.332804
Minimum1.4
Maximum84.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:33.307077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.24
Q16
median14
Q332.7
95-th percentile62.36
Maximum84.5
Range83.1
Interquartile range (IQR)26.7

Descriptive statistics

Standard deviation19.548058
Coefficient of variation (CV)0.91633795
Kurtosis0.57417273
Mean21.332804
Median Absolute Deviation (MAD)10.4
Skewness1.1585542
Sum4031.9
Variance382.12658
MonotonicityNot monotonic
2023-07-29T21:09:33.580382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 4
 
2.1%
3.1 4
 
2.1%
6.1 4
 
2.1%
12.4 3
 
1.5%
3.3 3
 
1.5%
2.6 3
 
1.5%
6.4 3
 
1.5%
9.8 3
 
1.5%
13.6 3
 
1.5%
2.7 3
 
1.5%
Other values (134) 156
80.0%
(Missing) 6
 
3.1%
ValueCountFrequency (%)
1.4 1
 
0.5%
1.5 1
 
0.5%
1.7 2
1.0%
1.8 1
 
0.5%
1.9 2
1.0%
2.1 2
1.0%
2.2 1
 
0.5%
2.3 2
1.0%
2.5 1
 
0.5%
2.6 3
1.5%
ValueCountFrequency (%)
84.5 1
0.5%
78.5 1
0.5%
76.6 1
0.5%
75.7 1
0.5%
71.4 1
0.5%
68.2 1
0.5%
65.7 1
0.5%
64.9 1
0.5%
63.7 1
0.5%
62.6 1
0.5%

Largest city
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct188
Distinct (%)99.5%
Missing6
Missing (%)3.1%
Memory size12.7 KiB
S����
 
2
Kigali
 
1
Moscow
 
1
Auckland
 
1
Managua
 
1
Other values (183)
183 

Length

Max length23
Median length20
Mean length8.3227513
Min length4

Characters and Unicode

Total characters1573
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)98.9%

Sample

1st rowKabul
2nd rowTirana
3rd rowAlgiers
4th rowAndorra la Vella
5th rowLuanda

Common Values

ValueCountFrequency (%)
S���� 2
 
1.0%
Kigali 1
 
0.5%
Moscow 1
 
0.5%
Auckland 1
 
0.5%
Managua 1
 
0.5%
Niamey 1
 
0.5%
Lagos 1
 
0.5%
Pyongyang 1
 
0.5%
Skopje 1
 
0.5%
Oslo 1
 
0.5%
Other values (178) 178
91.3%
(Missing) 6
 
3.1%

Length

2023-07-29T21:09:33.868576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 11
 
4.6%
san 3
 
1.3%
port 3
 
1.3%
st 2
 
0.8%
la 2
 
0.8%
s���� 2
 
0.8%
cotonou 1
 
0.4%
belize 1
 
0.4%
vella 1
 
0.4%
luanda 1
 
0.4%
Other values (211) 211
88.7%

Most occurring characters

ValueCountFrequency (%)
a 222
 
14.1%
o 109
 
6.9%
i 108
 
6.9%
n 103
 
6.5%
e 86
 
5.5%
r 83
 
5.3%
u 64
 
4.1%
s 62
 
3.9%
t 58
 
3.7%
l 51
 
3.2%
Other values (47) 627
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1233
78.4%
Uppercase Letter 234
 
14.9%
Space Separator 49
 
3.1%
Other Symbol 42
 
2.7%
Other Punctuation 13
 
0.8%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 222
18.0%
o 109
 
8.8%
i 108
 
8.8%
n 103
 
8.4%
e 86
 
7.0%
r 83
 
6.7%
u 64
 
5.2%
s 62
 
5.0%
t 58
 
4.7%
l 51
 
4.1%
Other values (16) 287
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 29
12.4%
C 27
11.5%
B 25
10.7%
M 20
 
8.5%
P 16
 
6.8%
A 15
 
6.4%
L 13
 
5.6%
T 13
 
5.6%
K 12
 
5.1%
D 11
 
4.7%
Other values (15) 53
22.6%
Other Punctuation
ValueCountFrequency (%)
, 8
61.5%
' 3
 
23.1%
. 2
 
15.4%
Space Separator
ValueCountFrequency (%)
49
100.0%
Other Symbol
ValueCountFrequency (%)
� 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1467
93.3%
Common 106
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 222
15.1%
o 109
 
7.4%
i 108
 
7.4%
n 103
 
7.0%
e 86
 
5.9%
r 83
 
5.7%
u 64
 
4.4%
s 62
 
4.2%
t 58
 
4.0%
l 51
 
3.5%
Other values (41) 521
35.5%
Common
ValueCountFrequency (%)
49
46.2%
� 42
39.6%
, 8
 
7.5%
' 3
 
2.8%
. 2
 
1.9%
- 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1531
97.3%
Specials 42
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 222
 
14.5%
o 109
 
7.1%
i 108
 
7.1%
n 103
 
6.7%
e 86
 
5.6%
r 83
 
5.4%
u 64
 
4.2%
s 62
 
4.0%
t 58
 
3.8%
l 51
 
3.3%
Other values (46) 585
38.2%
Specials
ValueCountFrequency (%)
� 42
100.0%

Life expectancy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct134
Distinct (%)71.7%
Missing8
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean72.279679
Minimum52.8
Maximum85.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:34.133865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum52.8
5-th percentile58.55
Q167
median73.2
Q377.5
95-th percentile82.7
Maximum85.4
Range32.6
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation7.4836606
Coefficient of variation (CV)0.10353755
Kurtosis-0.39712234
Mean72.279679
Median Absolute Deviation (MAD)5.1
Skewness-0.52804756
Sum13516.3
Variance56.005176
MonotonicityNot monotonic
2023-07-29T21:09:34.375255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.5 5
 
2.6%
61.2 4
 
2.1%
74.4 3
 
1.5%
75.7 3
 
1.5%
72.8 3
 
1.5%
81.3 3
 
1.5%
71.6 3
 
1.5%
71.8 3
 
1.5%
76.8 3
 
1.5%
63.8 2
 
1.0%
Other values (124) 155
79.5%
(Missing) 8
 
4.1%
ValueCountFrequency (%)
52.8 1
0.5%
53.7 1
0.5%
54 1
0.5%
54.3 2
1.0%
57.1 1
0.5%
57.4 1
0.5%
57.6 1
0.5%
58 1
0.5%
58.4 1
0.5%
58.9 2
1.0%
ValueCountFrequency (%)
85.4 1
0.5%
84.2 1
0.5%
83.6 1
0.5%
83.3 1
0.5%
83.1 1
0.5%
83 1
0.5%
82.9 1
0.5%
82.8 2
1.0%
82.7 2
1.0%
82.6 1
0.5%

Maternal mortality ratio
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct114
Distinct (%)63.0%
Missing14
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean160.39227
Minimum2
Maximum1150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:34.607634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q113
median53
Q3186
95-th percentile638
Maximum1150
Range1148
Interquartile range (IQR)173

Descriptive statistics

Standard deviation233.50202
Coefficient of variation (CV)1.4558185
Kurtosis4.8716649
Mean160.39227
Median Absolute Deviation (MAD)47
Skewness2.1715103
Sum29031
Variance54523.195
MonotonicityNot monotonic
2023-07-29T21:09:34.843964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 8
 
4.1%
19 6
 
3.1%
17 5
 
2.6%
3 5
 
2.6%
8 5
 
2.6%
4 4
 
2.1%
29 4
 
2.1%
6 4
 
2.1%
7 4
 
2.1%
10 4
 
2.1%
Other values (104) 132
67.7%
(Missing) 14
 
7.2%
ValueCountFrequency (%)
2 4
2.1%
3 5
2.6%
4 4
2.1%
5 8
4.1%
6 4
2.1%
7 4
2.1%
8 5
2.6%
9 3
 
1.5%
10 4
2.1%
11 1
 
0.5%
ValueCountFrequency (%)
1150 1
0.5%
1140 1
0.5%
1120 1
0.5%
917 1
0.5%
829 2
1.0%
766 1
0.5%
667 1
0.5%
661 1
0.5%
638 1
0.5%
617 1
0.5%

Minimum wage
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct114
Distinct (%)76.0%
Missing45
Missing (%)23.1%
Memory size10.8 KiB
$2.00
 
3
$0.41
 
3
$0.34
 
2
$2.25
 
2
$0.53
 
2
Other values (109)
138 

Length

Max length7
Median length6
Mean length6.06
Min length6

Characters and Unicode

Total characters909
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)53.3%

Sample

1st row$0.43
2nd row$1.12
3rd row$0.95
4th row$6.63
5th row$0.71

Common Values

ValueCountFrequency (%)
$2.00 3
 
1.5%
$0.41 3
 
1.5%
$0.34 2
 
1.0%
$2.25 2
 
1.0%
$0.53 2
 
1.0%
$0.35 2
 
1.0%
$1.12 2
 
1.0%
$0.60 2
 
1.0%
$0.25 2
 
1.0%
$1.23 2
 
1.0%
Other values (104) 128
65.6%
(Missing) 45
 
23.1%

Length

2023-07-29T21:09:35.060426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.00 3
 
2.0%
0.41 3
 
2.0%
0.71 2
 
1.3%
0.36 2
 
1.3%
0.54 2
 
1.3%
1.53 2
 
1.3%
0.29 2
 
1.3%
0.95 2
 
1.3%
0.23 2
 
1.3%
1.49 2
 
1.3%
Other values (104) 128
85.3%

Most occurring characters

ValueCountFrequency (%)
$ 150
16.5%
. 150
16.5%
150
16.5%
0 110
12.1%
1 76
8.4%
3 53
 
5.8%
5 43
 
4.7%
2 42
 
4.6%
4 34
 
3.7%
6 30
 
3.3%
Other values (3) 71
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 459
50.5%
Currency Symbol 150
 
16.5%
Other Punctuation 150
 
16.5%
Space Separator 150
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110
24.0%
1 76
16.6%
3 53
11.5%
5 43
 
9.4%
2 42
 
9.2%
4 34
 
7.4%
6 30
 
6.5%
9 25
 
5.4%
8 24
 
5.2%
7 22
 
4.8%
Currency Symbol
ValueCountFrequency (%)
$ 150
100.0%
Other Punctuation
ValueCountFrequency (%)
. 150
100.0%
Space Separator
ValueCountFrequency (%)
150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 909
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
$ 150
16.5%
. 150
16.5%
150
16.5%
0 110
12.1%
1 76
8.4%
3 53
 
5.8%
5 43
 
4.7%
2 42
 
4.6%
4 34
 
3.7%
6 30
 
3.3%
Other values (3) 71
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
$ 150
16.5%
. 150
16.5%
150
16.5%
0 110
12.1%
1 76
8.4%
3 53
 
5.8%
5 43
 
4.7%
2 42
 
4.6%
4 34
 
3.7%
6 30
 
3.3%
Other values (3) 71
7.8%

Official language
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct77
Distinct (%)39.7%
Missing1
Missing (%)0.5%
Memory size12.4 KiB
English
31 
French
25 
Spanish
19 
Arabic
18 
Portuguese
 
7
Other values (72)
94 

Length

Max length22
Median length20
Mean length7.4484536
Min length3

Characters and Unicode

Total characters1445
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)30.4%

Sample

1st rowPashto
2nd rowAlbanian
3rd rowArabic
4th rowCatalan
5th rowPortuguese

Common Values

ValueCountFrequency (%)
English 31
15.9%
French 25
 
12.8%
Spanish 19
 
9.7%
Arabic 18
 
9.2%
Portuguese 7
 
3.6%
None 4
 
2.1%
German 4
 
2.1%
Swahili 4
 
2.1%
Russian 4
 
2.1%
Italian 3
 
1.5%
Other values (67) 75
38.5%

Length

2023-07-29T21:09:35.705697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 32
 
15.3%
french 25
 
12.0%
arabic 20
 
9.6%
spanish 19
 
9.1%
portuguese 7
 
3.3%
language 7
 
3.3%
none 4
 
1.9%
german 4
 
1.9%
swahili 4
 
1.9%
russian 4
 
1.9%
Other values (70) 83
39.7%

Most occurring characters

ValueCountFrequency (%)
n 164
 
11.3%
a 148
 
10.2%
i 143
 
9.9%
h 100
 
6.9%
e 99
 
6.9%
r 90
 
6.2%
s 88
 
6.1%
g 64
 
4.4%
l 62
 
4.3%
c 53
 
3.7%
Other values (36) 434
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1226
84.8%
Uppercase Letter 204
 
14.1%
Space Separator 15
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 164
13.4%
a 148
12.1%
i 143
11.7%
h 100
8.2%
e 99
8.1%
r 90
7.3%
s 88
7.2%
g 64
 
5.2%
l 62
 
5.1%
c 53
 
4.3%
Other values (15) 215
17.5%
Uppercase Letter
ValueCountFrequency (%)
E 33
16.2%
S 33
16.2%
F 26
12.7%
A 25
12.3%
P 12
 
5.9%
M 10
 
4.9%
T 8
 
3.9%
G 7
 
3.4%
I 6
 
2.9%
N 6
 
2.9%
Other values (10) 38
18.6%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1430
99.0%
Common 15
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 164
 
11.5%
a 148
 
10.3%
i 143
 
10.0%
h 100
 
7.0%
e 99
 
6.9%
r 90
 
6.3%
s 88
 
6.2%
g 64
 
4.5%
l 62
 
4.3%
c 53
 
3.7%
Other values (35) 419
29.3%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 164
 
11.3%
a 148
 
10.2%
i 143
 
9.9%
h 100
 
6.9%
e 99
 
6.9%
r 90
 
6.2%
s 88
 
6.1%
g 64
 
4.4%
l 62
 
4.3%
c 53
 
3.7%
Other values (36) 434
30.0%

Out of pocket health expenditure
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct160
Distinct (%)85.1%
Missing7
Missing (%)3.6%
Memory size11.9 KiB
40.50%
 
3
15.20%
 
3
36.70%
 
3
36.00%
 
2
22.80%
 
2
Other values (155)
175 

Length

Max length6
Median length6
Mean length5.9148936
Min length5

Characters and Unicode

Total characters1112
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)71.8%

Sample

1st row78.40%
2nd row56.90%
3rd row28.10%
4th row36.40%
5th row33.40%

Common Values

ValueCountFrequency (%)
40.50% 3
 
1.5%
15.20% 3
 
1.5%
36.70% 3
 
1.5%
36.00% 2
 
1.0%
22.80% 2
 
1.0%
6.80% 2
 
1.0%
43.70% 2
 
1.0%
12.50% 2
 
1.0%
14.80% 2
 
1.0%
18.30% 2
 
1.0%
Other values (150) 165
84.6%
(Missing) 7
 
3.6%

Length

2023-07-29T21:09:35.931058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40.50 3
 
1.6%
36.70 3
 
1.6%
15.20 3
 
1.6%
25.90 2
 
1.1%
36.40 2
 
1.1%
2.50 2
 
1.1%
32.10 2
 
1.1%
25.10 2
 
1.1%
17.60 2
 
1.1%
19.60 2
 
1.1%
Other values (150) 165
87.8%

Most occurring characters

ValueCountFrequency (%)
0 219
19.7%
. 188
16.9%
% 188
16.9%
1 83
 
7.5%
3 76
 
6.8%
2 74
 
6.7%
4 61
 
5.5%
6 54
 
4.9%
5 52
 
4.7%
7 47
 
4.2%
Other values (2) 70
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 736
66.2%
Other Punctuation 376
33.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 219
29.8%
1 83
 
11.3%
3 76
 
10.3%
2 74
 
10.1%
4 61
 
8.3%
6 54
 
7.3%
5 52
 
7.1%
7 47
 
6.4%
8 45
 
6.1%
9 25
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 188
50.0%
% 188
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 219
19.7%
. 188
16.9%
% 188
16.9%
1 83
 
7.5%
3 76
 
6.8%
2 74
 
6.7%
4 61
 
5.5%
6 54
 
4.9%
5 52
 
4.7%
7 47
 
4.2%
Other values (2) 70
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 219
19.7%
. 188
16.9%
% 188
16.9%
1 83
 
7.5%
3 76
 
6.8%
2 74
 
6.7%
4 61
 
5.5%
6 54
 
4.9%
5 52
 
4.7%
7 47
 
4.2%
Other values (2) 70
 
6.3%

Physicians per thousand
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct152
Distinct (%)80.9%
Missing7
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1.8398404
Minimum0.01
Maximum8.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-29T21:09:36.142532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.0635
Q10.3325
median1.46
Q32.935
95-th percentile4.8995
Maximum8.42
Range8.41
Interquartile range (IQR)2.6025

Descriptive statistics

Standard deviation1.684261
Coefficient of variation (CV)0.91543866
Kurtosis0.83405658
Mean1.8398404
Median Absolute Deviation (MAD)1.235
Skewness1.0078449
Sum345.89
Variance2.8367353
MonotonicityNot monotonic
2023-07-29T21:09:36.395815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 6
 
3.1%
0.04 4
 
2.1%
0.07 4
 
2.1%
2.61 3
 
1.5%
0.17 3
 
1.5%
0.13 3
 
1.5%
0.42 3
 
1.5%
0.06 2
 
1.0%
0.23 2
 
1.0%
0.1 2
 
1.0%
Other values (142) 156
80.0%
(Missing) 7
 
3.6%
ValueCountFrequency (%)
0.01 1
 
0.5%
0.02 1
 
0.5%
0.03 1
 
0.5%
0.04 4
2.1%
0.05 1
 
0.5%
0.06 2
 
1.0%
0.07 4
2.1%
0.08 6
3.1%
0.09 1
 
0.5%
0.1 2
 
1.0%
ValueCountFrequency (%)
8.42 1
0.5%
7.12 1
0.5%
6.56 1
0.5%
6.35 1
0.5%
6.11 1
0.5%
5.48 1
0.5%
5.19 1
0.5%
5.17 1
0.5%
5.12 1
0.5%
5.05 1
0.5%

Population
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct194
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Memory size12.7 KiB
38,041,754
 
1
182,790
 
1
6,545,502
 
1
23,310,715
 
1
200,963,599
 
1
Other values (189)
189 

Length

Max length13
Median length11
Mean length9.0721649
Min length3

Characters and Unicode

Total characters1760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)100.0%

Sample

1st row38,041,754
2nd row2,854,191
3rd row43,053,054
4th row77,142
5th row31,825,295

Common Values

ValueCountFrequency (%)
38,041,754 1
 
0.5%
182,790 1
 
0.5%
6,545,502 1
 
0.5%
23,310,715 1
 
0.5%
200,963,599 1
 
0.5%
25,666,161 1
 
0.5%
1,836,713 1
 
0.5%
5,347,896 1
 
0.5%
5,266,535 1
 
0.5%
216,565,318 1
 
0.5%
Other values (184) 184
94.4%

Length

2023-07-29T21:09:36.651136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
38,041,754 1
 
0.5%
10,738,958 1
 
0.5%
389,482 1
 
0.5%
43,053,054 1
 
0.5%
77,142 1
 
0.5%
31,825,295 1
 
0.5%
97,118 1
 
0.5%
44,938,712 1
 
0.5%
2,957,731 1
 
0.5%
25,766,605 1
 
0.5%
Other values (184) 184
94.8%

Most occurring characters

ValueCountFrequency (%)
, 350
19.9%
1 174
9.9%
3 163
9.3%
5 157
8.9%
0 156
8.9%
6 147
8.4%
2 136
 
7.7%
7 127
 
7.2%
8 126
 
7.2%
4 112
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1410
80.1%
Other Punctuation 350
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 174
12.3%
3 163
11.6%
5 157
11.1%
0 156
11.1%
6 147
10.4%
2 136
9.6%
7 127
9.0%
8 126
8.9%
4 112
7.9%
9 112
7.9%
Other Punctuation
ValueCountFrequency (%)
, 350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 350
19.9%
1 174
9.9%
3 163
9.3%
5 157
8.9%
0 156
8.9%
6 147
8.4%
2 136
 
7.7%
7 127
 
7.2%
8 126
 
7.2%
4 112
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 350
19.9%
1 174
9.9%
3 163
9.3%
5 157
8.9%
0 156
8.9%
6 147
8.4%
2 136
 
7.7%
7 127
 
7.2%
8 126
 
7.2%
4 112
 
6.4%

Population: Labor force participation (%)
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct145
Distinct (%)82.4%
Missing19
Missing (%)9.7%
Memory size11.5 KiB
68.80%
 
3
65.10%
 
3
72.00%
 
3
68.00%
 
2
83.80%
 
2
Other values (140)
163 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1056
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)66.5%

Sample

1st row48.90%
2nd row55.70%
3rd row41.20%
4th row77.50%
5th row61.30%

Common Values

ValueCountFrequency (%)
68.80% 3
 
1.5%
65.10% 3
 
1.5%
72.00% 3
 
1.5%
68.00% 2
 
1.0%
83.80% 2
 
1.0%
64.00% 2
 
1.0%
69.90% 2
 
1.0%
72.40% 2
 
1.0%
56.50% 2
 
1.0%
66.40% 2
 
1.0%
Other values (135) 153
78.5%
(Missing) 19
 
9.7%

Length

2023-07-29T21:09:36.930386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
68.80 3
 
1.7%
72.00 3
 
1.7%
65.10 3
 
1.7%
68.00 2
 
1.1%
59.10 2
 
1.1%
53.60 2
 
1.1%
67.30 2
 
1.1%
74.60 2
 
1.1%
61.70 2
 
1.1%
59.70 2
 
1.1%
Other values (135) 153
86.9%

Most occurring characters

ValueCountFrequency (%)
0 210
19.9%
. 176
16.7%
% 176
16.7%
6 105
9.9%
5 75
 
7.1%
7 69
 
6.5%
4 58
 
5.5%
8 47
 
4.5%
3 40
 
3.8%
9 36
 
3.4%
Other values (2) 64
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 704
66.7%
Other Punctuation 352
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 210
29.8%
6 105
14.9%
5 75
 
10.7%
7 69
 
9.8%
4 58
 
8.2%
8 47
 
6.7%
3 40
 
5.7%
9 36
 
5.1%
1 33
 
4.7%
2 31
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 176
50.0%
% 176
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1056
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 210
19.9%
. 176
16.7%
% 176
16.7%
6 105
9.9%
5 75
 
7.1%
7 69
 
6.5%
4 58
 
5.5%
8 47
 
4.5%
3 40
 
3.8%
9 36
 
3.4%
Other values (2) 64
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 210
19.9%
. 176
16.7%
% 176
16.7%
6 105
9.9%
5 75
 
7.1%
7 69
 
6.5%
4 58
 
5.5%
8 47
 
4.5%
3 40
 
3.8%
9 36
 
3.4%
Other values (2) 64
 
6.1%

Tax revenue (%)
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct119
Distinct (%)70.4%
Missing26
Missing (%)13.3%
Memory size11.3 KiB
19.50%
 
4
13.60%
 
3
23.00%
 
3
20.10%
 
3
18.60%
 
3
Other values (114)
153 

Length

Max length6
Median length6
Mean length5.852071
Min length5

Characters and Unicode

Total characters989
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)45.6%

Sample

1st row9.30%
2nd row18.60%
3rd row37.20%
4th row9.20%
5th row16.50%

Common Values

ValueCountFrequency (%)
19.50% 4
 
2.1%
13.60% 3
 
1.5%
23.00% 3
 
1.5%
20.10% 3
 
1.5%
18.60% 3
 
1.5%
14.20% 3
 
1.5%
10.20% 3
 
1.5%
17.30% 2
 
1.0%
11.80% 2
 
1.0%
24.20% 2
 
1.0%
Other values (109) 141
72.3%
(Missing) 26
 
13.3%

Length

2023-07-29T21:09:37.161767image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19.50 4
 
2.4%
23.00 3
 
1.8%
20.10 3
 
1.8%
18.60 3
 
1.8%
14.20 3
 
1.8%
10.20 3
 
1.8%
13.60 3
 
1.8%
27.50 2
 
1.2%
25.40 2
 
1.2%
17.80 2
 
1.2%
Other values (109) 141
83.4%

Most occurring characters

ValueCountFrequency (%)
0 215
21.7%
. 169
17.1%
% 169
17.1%
1 130
13.1%
2 85
 
8.6%
4 35
 
3.5%
9 33
 
3.3%
8 33
 
3.3%
5 32
 
3.2%
3 31
 
3.1%
Other values (2) 57
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 651
65.8%
Other Punctuation 338
34.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 215
33.0%
1 130
20.0%
2 85
 
13.1%
4 35
 
5.4%
9 33
 
5.1%
8 33
 
5.1%
5 32
 
4.9%
3 31
 
4.8%
6 31
 
4.8%
7 26
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 169
50.0%
% 169
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 215
21.7%
. 169
17.1%
% 169
17.1%
1 130
13.1%
2 85
 
8.6%
4 35
 
3.5%
9 33
 
3.3%
8 33
 
3.3%
5 32
 
3.2%
3 31
 
3.1%
Other values (2) 57
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 215
21.7%
. 169
17.1%
% 169
17.1%
1 130
13.1%
2 85
 
8.6%
4 35
 
3.5%
9 33
 
3.3%
8 33
 
3.3%
5 32
 
3.2%
3 31
 
3.1%
Other values (2) 57
 
5.8%

Total tax rate
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct156
Distinct (%)85.2%
Missing12
Missing (%)6.2%
Memory size11.8 KiB
36.60%
 
4
49.70%
 
3
55.40%
 
2
36.20%
 
2
30.60%
 
2
Other values (151)
170 

Length

Max length7
Median length6
Mean length5.9945355
Min length5

Characters and Unicode

Total characters1097
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)72.1%

Sample

1st row71.40%
2nd row36.60%
3rd row66.10%
4th row49.10%
5th row43.00%

Common Values

ValueCountFrequency (%)
36.60% 4
 
2.1%
49.70% 3
 
1.5%
55.40% 2
 
1.0%
36.20% 2
 
1.0%
30.60% 2
 
1.0%
42.70% 2
 
1.0%
41.20% 2
 
1.0%
30.10% 2
 
1.0%
37.00% 2
 
1.0%
83.70% 2
 
1.0%
Other values (146) 160
82.1%
(Missing) 12
 
6.2%

Length

2023-07-29T21:09:37.417083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36.60 4
 
2.2%
49.70 3
 
1.6%
41.80 2
 
1.1%
48.80 2
 
1.1%
49.10 2
 
1.1%
32.60 2
 
1.1%
37.90 2
 
1.1%
38.70 2
 
1.1%
47.80 2
 
1.1%
37.20 2
 
1.1%
Other values (146) 160
87.4%

Most occurring characters

ValueCountFrequency (%)
0 219
20.0%
. 183
16.7%
% 183
16.7%
3 100
9.1%
4 74
 
6.7%
2 68
 
6.2%
7 54
 
4.9%
1 53
 
4.8%
6 52
 
4.7%
5 48
 
4.4%
Other values (2) 63
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
66.6%
Other Punctuation 366
33.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 219
30.0%
3 100
13.7%
4 74
 
10.1%
2 68
 
9.3%
7 54
 
7.4%
1 53
 
7.3%
6 52
 
7.1%
5 48
 
6.6%
8 36
 
4.9%
9 27
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 183
50.0%
% 183
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1097
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 219
20.0%
. 183
16.7%
% 183
16.7%
3 100
9.1%
4 74
 
6.7%
2 68
 
6.2%
7 54
 
4.9%
1 53
 
4.8%
6 52
 
4.7%
5 48
 
4.4%
Other values (2) 63
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 219
20.0%
. 183
16.7%
% 183
16.7%
3 100
9.1%
4 74
 
6.7%
2 68
 
6.2%
7 54
 
4.9%
1 53
 
4.8%
6 52
 
4.7%
5 48
 
4.4%
Other values (2) 63
 
5.7%

Unemployment rate
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct164
Distinct (%)93.2%
Missing19
Missing (%)9.7%
Memory size11.4 KiB
4.59%
 
3
6.33%
 
2
11.85%
 
2
4.34%
 
2
2.46%
 
2
Other values (159)
165 

Length

Max length6
Median length5
Mean length5.2272727
Min length5

Characters and Unicode

Total characters920
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)86.9%

Sample

1st row11.12%
2nd row12.33%
3rd row11.70%
4th row6.89%
5th row9.79%

Common Values

ValueCountFrequency (%)
4.59% 3
 
1.5%
6.33% 2
 
1.0%
11.85% 2
 
1.0%
4.34% 2
 
1.0%
2.46% 2
 
1.0%
5.56% 2
 
1.0%
5.36% 2
 
1.0%
3.32% 2
 
1.0%
3.47% 2
 
1.0%
4.11% 2
 
1.0%
Other values (154) 155
79.5%
(Missing) 19
 
9.7%

Length

2023-07-29T21:09:37.669409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4.59 3
 
1.7%
5.36 2
 
1.1%
4.20 2
 
1.1%
4.11 2
 
1.1%
3.47 2
 
1.1%
3.32 2
 
1.1%
6.33 2
 
1.1%
5.56 2
 
1.1%
4.34 2
 
1.1%
11.85 2
 
1.1%
Other values (154) 155
88.1%

Most occurring characters

ValueCountFrequency (%)
. 176
19.1%
% 176
19.1%
1 88
9.6%
3 75
8.2%
4 73
7.9%
2 62
 
6.7%
8 49
 
5.3%
5 48
 
5.2%
6 48
 
5.2%
0 47
 
5.1%
Other values (2) 78
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 568
61.7%
Other Punctuation 352
38.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 88
15.5%
3 75
13.2%
4 73
12.9%
2 62
10.9%
8 49
8.6%
5 48
8.5%
6 48
8.5%
0 47
8.3%
9 44
7.7%
7 34
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 176
50.0%
% 176
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 176
19.1%
% 176
19.1%
1 88
9.6%
3 75
8.2%
4 73
7.9%
2 62
 
6.7%
8 49
 
5.3%
5 48
 
5.2%
6 48
 
5.2%
0 47
 
5.1%
Other values (2) 78
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 176
19.1%
% 176
19.1%
1 88
9.6%
3 75
8.2%
4 73
7.9%
2 62
 
6.7%
8 49
 
5.3%
5 48
 
5.2%
6 48
 
5.2%
0 47
 
5.1%
Other values (2) 78
8.5%

Urban_population
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct190
Distinct (%)100.0%
Missing5
Missing (%)2.6%
Memory size12.5 KiB
9,797,273
 
1
1,162,834
 
1
3,846,137
 
1
3,850,231
 
1
102,806,948
 
1
Other values (185)
185 

Length

Max length11
Median length10
Mean length8.7684211
Min length5

Characters and Unicode

Total characters1666
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)100.0%

Sample

1st row9,797,273
2nd row1,747,593
3rd row31,510,100
4th row67,873
5th row21,061,025

Common Values

ValueCountFrequency (%)
9,797,273 1
 
0.5%
1,162,834 1
 
0.5%
3,846,137 1
 
0.5%
3,850,231 1
 
0.5%
102,806,948 1
 
0.5%
15,947,412 1
 
0.5%
4,418,218 1
 
0.5%
4,250,777 1
 
0.5%
79,927,762 1
 
0.5%
14,491 1
 
0.5%
Other values (180) 180
92.3%
(Missing) 5
 
2.6%

Length

2023-07-29T21:09:37.940683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,797,273 1
 
0.5%
5,616,165 1
 
0.5%
323,784 1
 
0.5%
31,510,100 1
 
0.5%
67,873 1
 
0.5%
21,061,025 1
 
0.5%
23,800 1
 
0.5%
41,339,571 1
 
0.5%
1,869,848 1
 
0.5%
21,844,756 1
 
0.5%
Other values (180) 180
94.7%

Most occurring characters

ValueCountFrequency (%)
, 335
20.1%
1 169
10.1%
4 148
8.9%
2 138
8.3%
5 132
 
7.9%
3 131
 
7.9%
8 129
 
7.7%
6 123
 
7.4%
0 123
 
7.4%
9 122
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1331
79.9%
Other Punctuation 335
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 169
12.7%
4 148
11.1%
2 138
10.4%
5 132
9.9%
3 131
9.8%
8 129
9.7%
6 123
9.2%
0 123
9.2%
9 122
9.2%
7 116
8.7%
Other Punctuation
ValueCountFrequency (%)
, 335
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1666
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 335
20.1%
1 169
10.1%
4 148
8.9%
2 138
8.3%
5 132
 
7.9%
3 131
 
7.9%
8 129
 
7.7%
6 123
 
7.4%
0 123
 
7.4%
9 122
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 335
20.1%
1 169
10.1%
4 148
8.9%
2 138
8.3%
5 132
 
7.9%
3 131
 
7.9%
8 129
 
7.7%
6 123
 
7.4%
0 123
 
7.4%
9 122
 
7.3%

Latitude
Real number (ℝ)

Distinct194
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean19.092351
Minimum-40.900557
Maximum64.963051
Zeros0
Zeros (%)0.0%
Negative41
Negative (%)21.0%
Memory size1.6 KiB
2023-07-29T21:09:38.167077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-40.900557
5-th percentile-22.548682
Q14.5441747
median17.273849
Q340.124603
95-th percentile55.242453
Maximum64.963051
Range105.86361
Interquartile range (IQR)35.580429

Descriptive statistics

Standard deviation23.961779
Coefficient of variation (CV)1.255046
Kurtosis-0.61350317
Mean19.092351
Median Absolute Deviation (MAD)18.070331
Skewness-0.25709386
Sum3703.9162
Variance574.16688
MonotonicityNot monotonic
2023-07-29T21:09:38.434362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.93911 1
 
0.5%
13.909444 1
 
0.5%
12.865416 1
 
0.5%
17.607789 1
 
0.5%
9.081999 1
 
0.5%
40.339852 1
 
0.5%
41.608635 1
 
0.5%
60.472024 1
 
0.5%
21.4735329 1
 
0.5%
30.375321 1
 
0.5%
Other values (184) 184
94.4%
ValueCountFrequency (%)
-40.900557 1
0.5%
-38.416097 1
0.5%
-35.675147 1
0.5%
-32.522779 1
0.5%
-30.559482 1
0.5%
-29.609988 1
0.5%
-26.522503 1
0.5%
-25.274398 1
0.5%
-23.442503 1
0.5%
-22.95764 1
0.5%
ValueCountFrequency (%)
64.963051 1
0.5%
61.92411 1
0.5%
61.52401 1
0.5%
60.472024 1
0.5%
60.128161 1
0.5%
58.595272 1
0.5%
56.879635 1
0.5%
56.26392 1
0.5%
56.130366 1
0.5%
55.378051 1
0.5%

Longitude
Real number (ℝ)

Distinct194
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean20.232434
Minimum-175.19824
Maximum178.06503
Zeros0
Zeros (%)0.0%
Negative56
Negative (%)28.7%
Memory size1.6 KiB
2023-07-29T21:09:38.651817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-175.19824
5-th percentile-85.569366
Q1-7.9414955
median20.972652
Q348.281523
95-th percentile135.86716
Maximum178.06503
Range353.26327
Interquartile range (IQR)56.223018

Descriptive statistics

Standard deviation66.71611
Coefficient of variation (CV)3.2974831
Kurtosis0.406904
Mean20.232434
Median Absolute Deviation (MAD)28.631189
Skewness-0.056581627
Sum3925.0923
Variance4451.0394
MonotonicityNot monotonic
2023-07-29T21:09:38.904105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.709953 1
 
0.5%
-60.978893 1
 
0.5%
-85.207229 1
 
0.5%
8.081666 1
 
0.5%
8.675277 1
 
0.5%
127.510093 1
 
0.5%
21.745275 1
 
0.5%
8.468946 1
 
0.5%
55.975413 1
 
0.5%
69.345116 1
 
0.5%
Other values (184) 184
94.4%
ValueCountFrequency (%)
-175.198242 1
0.5%
-172.104629 1
0.5%
-157.3768317 1
0.5%
-106.346771 1
0.5%
-102.552784 1
0.5%
-95.712891 1
0.5%
-90.230759 1
0.5%
-88.89653 1
0.5%
-88.49765 1
0.5%
-86.241905 1
0.5%
ValueCountFrequency (%)
178.065032 1
0.5%
177.64933 1
0.5%
174.885971 1
0.5%
171.184478 1
0.5%
166.959158 1
0.5%
166.931503 1
0.5%
160.156194 1
0.5%
150.550812 1
0.5%
143.95555 1
0.5%
138.252924 1
0.5%

Interactions

2023-07-29T21:09:20.695759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:03.923616image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:06.006049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:07.841140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:09.603425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:11.443504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:15.070801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:17.359679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:19.158870image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:20.926142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:04.152005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:06.260367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:08.029634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:09.901627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:11.671891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:15.364017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:17.579093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:19.353348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:21.136580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:04.413306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:06.477787image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:08.219130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:10.111067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:11.904272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:15.601384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:17.813466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:19.520898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:21.359982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:04.644688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:06.714155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:08.409619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:10.320509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:12.229401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:15.847724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:18.019915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:19.693475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:21.544486image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:04.874075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:06.922594image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:08.567197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:10.509004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:12.494691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:16.094066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:18.226362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:19.859993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:21.769886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:05.084512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:07.111093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:08.761677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:10.727418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:12.816833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:16.360355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:18.467716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:20.030535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:21.993286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:05.352793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:07.297593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:08.978100image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:10.934864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:14.190158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:16.613674image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:18.655214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:20.197091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:22.178790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:05.567220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:07.470132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:09.176568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:11.103415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:14.459437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:16.861015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:18.834735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:20.362649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:22.382247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:05.792619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:07.644666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:09.382017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:11.273956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:14.762627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:17.101372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:18.994307image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-29T21:09:20.520225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-29T21:09:39.102615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Birth RateCalling CodeFertility RateInfant mortalityLife expectancyMaternal mortality ratioPhysicians per thousandLatitudeLongitudeCPI Change (%)Official language
Birth Rate1.0000.1640.9810.896-0.8600.857-0.845-0.5960.0400.2800.000
Calling Code0.1641.0000.1650.106-0.1540.052-0.074-0.0440.3040.1260.526
Fertility Rate0.9810.1651.0000.867-0.8430.831-0.817-0.5790.0380.3900.000
Infant mortality0.8960.1060.8671.000-0.9260.926-0.862-0.5930.0630.3460.000
Life expectancy-0.860-0.154-0.843-0.9261.000-0.8890.8070.522-0.1150.2500.000
Maternal mortality ratio0.8570.0520.8310.926-0.8891.000-0.864-0.636-0.0360.3810.000
Physicians per thousand-0.845-0.074-0.817-0.8620.807-0.8641.0000.608-0.0610.0000.337
Latitude-0.596-0.044-0.579-0.5930.522-0.6360.6081.000-0.0240.0000.296
Longitude0.0400.3040.0380.063-0.115-0.036-0.061-0.0241.0000.0000.272
CPI Change (%)0.2800.1260.3900.3460.2500.3810.0000.0000.0001.0000.000
Official language0.0000.5260.0000.0000.0000.0000.3370.2960.2720.0001.000

Missing values

2023-07-29T21:09:22.765221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-29T21:09:23.680773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-29T21:09:24.848650image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryDensity (P/Km2)AbbreviationAgricultural Land( %)Land Area(Km2)Armed Forces sizeBirth RateCalling CodeCapital/Major CityCo2-EmissionsCPICPI Change (%)Currency-CodeFertility RateForested Area (%)Gasoline PriceGDPGross primary education enrollment (%)Gross tertiary education enrollment (%)Infant mortalityLargest cityLife expectancyMaternal mortality ratioMinimum wageOfficial languageOut of pocket health expenditurePhysicians per thousandPopulationPopulation: Labor force participation (%)Tax revenue (%)Total tax rateUnemployment rateUrban_populationLatitudeLongitude
0Afghanistan60AF58.10%652,230323,00032.4993.0Kabul8,672149.92.30%AFN4.472.10%$0.70$19,101,353,833104.00%9.70%47.9Kabul64.5638.0$0.43Pashto78.40%0.2838,041,75448.90%9.30%71.40%11.12%9,797,27333.93911067.709953
1Albania105AL43.10%28,7489,00011.78355.0Tirana4,536119.051.40%ALL1.6228.10%$1.36$15,278,077,447107.00%55.00%7.8Tirana78.515.0$1.12Albanian56.90%1.202,854,19155.70%18.60%36.60%12.33%1,747,59341.15333220.168331
2Algeria18DZ17.40%2,381,741317,00024.28213.0Algiers150,006151.362.00%DZD3.020.80%$0.28$169,988,236,398109.90%51.40%20.1Algiers76.7112.0$0.95Arabic28.10%1.7243,053,05441.20%37.20%66.10%11.70%31,510,10028.0338861.659626
3Andorra164AD40.00%468NaN7.20376.0Andorra la Vella469NaNNaNEUR1.2734.00%$1.51$3,154,057,987106.40%NaN2.7Andorra la VellaNaNNaN$6.63Catalan36.40%3.3377,142NaNNaNNaNNaN67,87342.5062851.521801
4Angola26AO47.50%1,246,700117,00040.73244.0Luanda34,693261.7317.10%AOA5.5246.30%$0.97$94,635,415,870113.50%9.30%51.6Luanda60.8241.0$0.71Portuguese33.40%0.2131,825,29577.50%9.20%49.10%6.89%21,061,025-11.20269217.873887
5Antigua and Barbuda223AG20.50%443015.331.0St. John's, Saint John557113.811.20%XCD1.9922.30%$0.99$1,727,759,259105.00%24.80%5.0St. John's, Saint John76.942.0$3.04English24.30%2.7697,118NaN16.50%43.00%NaN23,80017.060816-61.796428
6Argentina17AR54.30%2,780,400105,00017.0254.0Buenos Aires201,348232.7553.50%ARS2.269.80%$1.10$449,663,446,954109.70%90.00%8.8Buenos Aires76.539.0$3.35Spanish17.60%3.9644,938,71261.30%10.10%106.30%9.79%41,339,571-38.416097-63.616672
7Armenia104AM58.90%29,74349,00013.99374.0Yerevan5,156129.181.40%AMD1.7611.70%$0.77$13,672,802,15892.70%54.60%11.0Yerevan74.926.0$0.66Armenian81.60%4.402,957,73155.60%20.90%22.60%16.99%1,869,84840.06909945.038189
8Australia3AU48.20%7,741,22058,00012.6061.0Canberra375,908119.81.60%AUD1.7416.30%$0.93$1,392,680,589,329100.30%113.10%3.1Sydney82.76.0$13.59None19.60%3.6825,766,60565.50%23.00%47.40%5.27%21,844,756-25.274398133.775136
9Austria109AT32.40%83,87121,0009.7043.0Vienna61,448118.061.50%EUR1.4746.90%$1.20$446,314,739,528103.10%85.10%2.9Vienna81.65.0NaNGerman17.90%5.178,877,06760.70%25.40%51.40%4.67%5,194,41647.51623114.550072
CountryDensity (P/Km2)AbbreviationAgricultural Land( %)Land Area(Km2)Armed Forces sizeBirth RateCalling CodeCapital/Major CityCo2-EmissionsCPICPI Change (%)Currency-CodeFertility RateForested Area (%)Gasoline PriceGDPGross primary education enrollment (%)Gross tertiary education enrollment (%)Infant mortalityLargest cityLife expectancyMaternal mortality ratioMinimum wageOfficial languageOut of pocket health expenditurePhysicians per thousandPopulationPopulation: Labor force participation (%)Tax revenue (%)Total tax rateUnemployment rateUrban_populationLatitudeLongitude
185United Kingdom281GB71.70%243,610148,00011.0044.0London379,025119.621.70%GBP1.6813.10%$1.46$2,827,113,184,696101.20%60.00%3.6London81.37.0$10.13English14.80%2.8166,834,40562.80%25.50%30.60%3.85%55,908,31655.378051-3.435973
186United States36US44.40%9,833,5171,359,00011.601.0Washington, D.C.5,006,302117.247.50%USD1.7333.90%$0.71$21,427,700,000,000101.80%88.20%5.6New York City78.519.0$7.25None11.10%2.61328,239,52362.00%9.60%36.60%14.70%270,663,02837.090240-95.712891
187Uruguay20UY82.60%176,21522,00013.86598.0Montevideo6,766202.927.90%UYU1.9710.70%$1.50$56,045,912,952108.50%63.10%6.4Montevideo77.817.0$1.66Spanish16.20%5.053,461,73464.00%20.10%41.80%8.73%3,303,394-32.522779-55.765835
188Uzbekistan79UZ62.90%447,40068,00023.30998.0Tashkent91,811NaNNaNUZS2.427.50%$1.03$57,921,286,440104.20%10.10%19.1Tashkent71.629.0$0.24Uzbek42.70%2.3733,580,65065.10%14.80%31.60%5.92%16,935,72941.37749164.585262
189Vanuatu25VU15.30%12,189NaN29.60678.0Port Vila147117.132.80%VUV3.7836.10%$1.31$917,058,851109.30%4.70%22.3Port Vila70.372.0$1.56French8.90%0.17299,88269.90%17.80%8.50%4.39%76,152-15.376706166.959158
190Venezuela32VE24.50%912,050343,00017.8858.0Caracas164,1752,740.27254.90%VED2.2752.70%$0.00$482,359,318,76897.20%79.30%21.4Caracas72.1125.0$0.01Spanish45.80%1.9228,515,82959.70%NaN73.30%8.80%25,162,3686.423750-66.589730
191Vietnam314VN39.30%331,210522,00016.7584.0Hanoi192,668163.522.80%VND2.0548.10%$0.80$261,921,244,843110.60%28.50%16.5Ho Chi Minh City75.343.0$0.73Vietnamese43.50%0.8296,462,10677.40%19.10%37.60%2.01%35,332,14014.058324108.277199
192Yemen56YE44.60%527,96840,00030.45967.0Sanaa10,609157.588.10%YER3.791.00%$0.92$26,914,402,22493.60%10.20%42.9Sanaa66.1164.0NaNArabic81.00%0.3129,161,92238.00%NaN26.60%12.91%10,869,52315.55272748.516388
193Zambia25ZM32.10%752,61816,00036.19260.0Lusaka5,141212.319.20%ZMW4.6365.20%$1.40$23,064,722,44698.70%4.10%40.4Lusaka63.5213.0$0.24English27.50%1.1917,861,03074.60%16.20%15.60%11.43%7,871,713-13.13389727.849332
194Zimbabwe38ZW41.90%390,75751,00030.68263.0Harare10,983105.510.90%NaN3.6235.50%$1.34$21,440,758,800109.90%10.00%33.9Harare61.2458.0NaNShona25.80%0.2114,645,46883.10%20.70%31.60%4.95%4,717,305-19.01543829.154857